Token Classification
Transformers
PyTorch
TensorBoard
layoutlmv3
Generated from Trainer
Eval Results (legacy)
Instructions to use mijungkim/pasha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mijungkim/pasha with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mijungkim/pasha")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("mijungkim/pasha") model = AutoModelForTokenClassification.from_pretrained("mijungkim/pasha", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ce27212e5d5bae225e84679ff6690cd2fe857a5fa600a8a042a4fe4998d7091
- Size of remote file:
- 504 MB
- SHA256:
- 5721140773f7a80e19f44ce76450175a31e04b4d4c069dde22023832cb2e59ec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.